AI in Healthcare: Modernizing Legacy Systems and Improving Patient Workflows

Solo physician practices have become increasingly rare, declining steadily over the past 30 years. According to the AMA’s 2024 Physician Practice Benchmark Survey, only 42.2% of physicians were in physician-owned private practice, down 18 percentage points from 2012. The shift reflects the same pressures many providers feel every day: inadequate payment rates, costly resources, and growing regulatory and administrative demands.

Rising overhead costs and administrative burdens, combined with decreasing reimbursement rates, have pushed many physicians to join group practices. These larger organizations allow providers to share fixed costs and gain stronger negotiating power with hospitals and insurance companies.

Larger health systems are also better positioned to invest in essential information technologies, such as electronic health records (EHRs) and billing platforms. However, even these organizations must continuously improve efficiency while maintaining high standards of patient care. This is where artificial intelligence (AI) is beginning to play a critical role, especially when organizations pair the technology with the right AI consulting services and implementation strategy.

Many healthcare software systems are decades old and costly to replace or upgrade. For many organizations, legacy system modernization with AI offers a more practical path forward. Healthcare modernization depends on interoperability, not wholesale replacement. Standards such as HL7 FHIR help clinical and administrative data move between systems through more consistent, API-focused exchange. Billing workflows may also involve long-standing formats and forms, such as CMS-1450/UB-04 for institutional claims. Together, these standards give healthcare organizations a way to connect older systems with newer tools without uprooting every platform at once.

AI can act as a middleware layer, reading from and writing to these legacy systems to extend their capabilities without requiring full replacement. For organizations trying to modernize without starting over, this approach allows healthcare providers to move incrementally while minimizing disruption and cost.

Healthcare organizations generate large volumes of unstructured data, including clinical notes, prescriptions, discharge summaries, and physician dictations. AI can organize, categorize, and store this information in a structured way, making it accessible for analysis, billing, and care coordination without requiring changes to how clinicians document their work.

In healthcare, however, AI adoption must be governed carefully. Any system that touches protected health information needs clear access controls, audit trails, data minimization, and human review when clinical or financial decisions are at stake. The goal is not to replace professional judgment, but to reduce friction around the work that surrounds it: retrieving information, preparing documentation, flagging risk, and helping staff act sooner with better context.

Once structured, this data can power more efficient workflows. AI can:

• Automatically populate forms
• Submit prior authorizations to insurers
• Generate referrals
• Process insurance claims

In clinical settings, AI can also support decision-making by generating predictive alerts based on patient data. Advanced applications extend to analyzing radiology images and pathology slides, helping clinicians identify potential issues earlier and more accurately. For example, SPR’s work on AI solutions for faster cancer diagnostics demonstrates how AI can support complex clinical workflows that integrate pathology images, genomic data, and diagnostic decision-making.

AI is also transforming the patient experience. Chatbots and virtual assistants can:

• Collect patient demographic and insurance information
• Schedule appointments
• Answer common billing and treatment questions

More advanced patient engagement tools may also draw on AI agent development services to help organizations design assistants that can answer questions, route requests, and connect with existing scheduling or billing systems.

AI will not solve healthcare’s operational pressures on its own. Its value depends on where and how it is introduced. The strongest opportunities are often found in the seams between systems: the handoffs, forms, notes, claims, referrals, and patient communications that consume time but do not always require a clinician’s direct attention. With the right architecture, governance, and integration strategy, AI can help healthcare organizations make better use of the systems they already have while preparing for the systems they will need next.

Next step: Explore SPR’s AI In-Focus advisory offering.